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Affected files: STEM/AI/Neural Networks/CNN/Examples.md STEM/AI/Neural Networks/CNN/FCN/FCN.md STEM/AI/Neural Networks/CNN/FCN/ResNet.md STEM/AI/Neural Networks/CNN/FCN/Skip Connections.md STEM/AI/Neural Networks/CNN/GAN/DC-GAN.md STEM/AI/Neural Networks/CNN/GAN/GAN.md STEM/AI/Neural Networks/CNN/Interpretation.md STEM/AI/Neural Networks/CNN/UpConv.md STEM/AI/Neural Networks/Deep Learning.md STEM/AI/Neural Networks/MLP/MLP.md STEM/AI/Neural Networks/Properties+Capabilities.md STEM/AI/Neural Networks/SLP/Least Mean Square.md STEM/AI/Neural Networks/SLP/SLP.md STEM/AI/Neural Networks/Transformers/Transformers.md STEM/AI/Properties.md STEM/CS/Language Binding.md STEM/CS/Languages/dotNet.md STEM/Signal Proc/Image/Image Processing.md
22 lines
1013 B
Markdown
22 lines
1013 B
Markdown
- [Feedforward](../Architectures.md)
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- Single hidden layer can learn any function
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- Universal approximation theorem
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- Each hidden layer can operate as a different feature extraction layer
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- Lots of [weights](../Weight%20Init.md) to learn
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- [Back-Propagation](Back-Propagation.md) is supervised
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# Universal Approximation Theory
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A finite [feedforward](../Architectures.md) MLP with 1 hidden layer can in theory approximate any mathematical function
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- In practice not trainable with [BP](Back-Propagation.md)
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## Weight Matrix
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- Use matrix multiplication for layer output
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- TLU is hard limiter
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- $o_1$ to $o_4$ must all be one to overcome -3.5 bias and force output to 1
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- Can generate a non-linear [decision boundary](Decision%20Boundary.md) |